In the past few years, it has become increasingly evident that deep neural networks are not
resilient enough to withstand adversarial perturbations in input data, leaving them …

This article presents a comprehensive and practical guide for practitioners and end-users
working with Large Language Models (LLMs) in their downstream Natural Language …

Text data augmentation is an effective strategy for overcoming the challenge of limited
sample sizes in many natural language processing (NLP) tasks. This challenge is especially …

Foundation models (FM) have demonstrated remarkable performance across a wide range
of tasks (especially in the fields of natural language processing and computer vision) …

Drought is a common and costly natural disaster with broad social, economic, and
environmental impacts. Machine learning (ML) has been widely applied in scientific …

Transformer-based language models have achieved significant success in various domains.
However, the data-intensive nature of the transformer architecture requires much labeled …

Traffic forecasting is a critical service in Intelligent Transportation Systems (ITS). Utilizing
deep models to tackle this task relies heavily on data from traffic sensors or vehicle devices …

Abstract Car-following (CF) modeling, a fundamental component in microscopic traffic
simulation, has attracted increasing research interest in recent decades. In this study, we …

Over the past few years, significant advancements have been made in the field of machine
learning (ML) to address resource management, interference management, autonomy, and …

Multilingual neural machine translation models (MNMT) yield state-of-the-art performance
when evaluated on data from a domain and language pair seen at training time. However …